- Apply filtering techniques(gaussian filter, bilateral filter)
- Object detection(Sobel, Hough transform)
- Masking: merge, blur
- Morphological Operations (Dilataion, Erosion, Closing)
For the complete notebook visit: cv-ga1.ipynb
This project is divided into two task:
- Craft and Extract face Features from four celebrities using SIFT and PCA detector
- Train a model (kNN, SVM, RandomForest, CNN) using supervised learning on a dataset of 80 labelled images
- For testing the accuracy of the model a dataset of 1136 unlabelled images where provided
- Kaggle was used to run a competition among several models
| Actor Face | Label | Num Imgs |
|---|---|---|
| Jesse Eisenberg | 1 | 30 |
| Michael Cera (who arguably looks similar to Jesse Eisenberg) | 0 | 10 |
| Mila Kunis | 2 | 30 |
| Sarah Hyland (who arguably looks similar to Mila Kunis) | 0 | 10 |
- Visual Bag of Words are used to create fingerprints of each picture
- kMeans clustering is used to narrow down the features extracted from each face
- Implement t-SNE plots to reduce dimensionality of set and fine tune hyperparamters of SIFT extractor
-
Reduce the dimensionality of the feature space by projecting the data onto a lower-dimension
-
Use eigenfaces to reconstruct an actor's face using PCA features extractor
The Ensemble method implemented use a combination of kNN, SVM, and CNN to predict the label of an image. The model shows positive results achieving the following accuracies:
| Models | Training Set | Test Set |
|---|---|---|
| Ensembles | 0.86 | 0.81 |
| kNN | 0.83 | 0.66 |
| SVM | 0.75 | 0.76 |
| CNN | 0.89 | 0.78 |
It is concluded that a combination of models using majority vote over the labels using hard constraint yields an accuracy that is optimally. Put into words, 8 out of 10 times we are able to recognise/distinguish who actor is who.